Abstract 2414: Prognosis prediction of colorectal cancer using gene expression profiles

Abstract

Abstract Background: Investigation on prognostic markers for colorectal cancer (CRC) deserves efforts, but data from China are scarce. This study aimed to build a prognostic algorithm using differentially expressed gene (DEG) profiles and to compare it with the TNM staging system in their predictive accuracy for Chinese CRC prognosis. Methods: DEGs in six-paired tumor and corresponding normal tissues were determined using RNA-Sequencing. Subsequently, matched tumor and normal tissues from 127 Chinese patients were assayed for further validation. Univariate and multivariate Cox regressions were used to identify informative DEGs. A predictive index (PI) was derived as a linear combination of the products of the DEGs and their Cox regression coefficients. The predictive performance of the PI was evaluated with receiver operating characteristics (ROC) curves and the area under curves (AUCs). A logistic regression model was built, including both the DEGs-based PI and tumors’ TNM stages. Results: We identified significant associations of 13 DEGs (out of 75 candidate DEGs) with CRC survival. A PI based on these 13 DEGs (PI-13) predicted CRC survival probability more accurately than the TNM staging system [AUCs for 3-year survival probability 0.76 (95% confidence interval: 0.67, 0.83) vs. 0.63 (0.53, 0.71)] but comparable to a simplified PI (PI-5) using five DEGs (LOC646627, SCARA5, CDKN2A, ATP6V1A, and DNMT3B). The predictive accuracy was improved further by combining PI-5 and the TNM staging system [AUC for 3-year survival probability: 0.81 (0.73, 0.88)]. Conclusion: Prognosis prediction based on informative DEGs might yield a higher accuracy than the TNM staging system. Citation Format: Feixia Pan, Tianhui Chen, Xiaohui Sun, Kuanrong Li, Xiyi Jiang, Asta Försti, Yimin Zhu, Maode Lai. Prognosis prediction of colorectal cancer using gene expression profiles [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2414.